
***********************************************************************
***********************************************************************
*THIS REPLICATION FILE REPORT TABLES AND FIGURES IN THE APPENDIX*******
***********************************************************************
***********************************************************************

* IMPORTANT NOTICE: PLEASE SET YOUR WORKING DIRECTORY TO YOUR DESIGNATED FOLDER. THIS IS THE CRITICAL STEP TO ENSURE THE SUCESS OF REPLICATING THE RESULTS

global path " " // Put your path to the folder here.



est clear


***********************************************************************
***********************************************************************
*TABLE A1 AND A2 ARE PRODUCED BY R. PLEASE SEE REPLICATION FILE "Appendix_Table_1_and_2.R"
***********************************************************************
***********************************************************************





**********************************************************
**********************************************************
********************* Table A3 and A4 ********************
**********************************************************
**********************************************************


***** Table A3

use "$path/Summary_Data_1.dta", clear 


sum log_price log_area land_level  usage_year logtotal_asset logtotal_employee logtotal_debt soe

tab auction_method


bysort lpc: sum log_price log_area land_level  usage_year logtotal_asset logtotal_employee logtotal_debt soe

bysort lpc: tab auction_method


use "$path/Reserve_Price_Data.dta", clear


sum log_startprice

bysort lpc: sum log_startprice

use "$path/Premium_Data.dta", clear

sum premiumrate

bysort lpc: sum premiumrate


***** Table A4

use "$path/Land_Price_Data.dta", clear 


sum gpc lpc le npc cppcc gov_friend relative_pc 



**********************************************************
**********************************************************
********************* Table A5 ***************************
**********************************************************
**********************************************************

est clear
use "$path/Appendix_A5_A9_Data.dta", clear 


label variable auction "Invited \& Bilateral"
label variable two_auction "Two-stage Auction"



eststo: xi: reghdfe log_price auction  pc_percent log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt, ab(land_purpose   year land_level firmid cityid usage_year ind) vce(cluster cityid firmid) keepsin

eststo: xi: reghdfe log_price auction two_auction  pc_percent log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt, ab(land_purpose   year land_level firmid cityid usage_year ind) vce(cluster cityid firmid) keepsin


******** Table A5 

esttab using "$path/Appendix_Table_A5.tex", label replace compress star(* 0.10 ** 0.05 *** 0.01) /// 
keep(auction two_auction) title(Effect of Auction Method on Land Prices)

est clear




**********************************************************
**********************************************************
********************* Table A6 ***************************
**********************************************************
**********************************************************

use "$path/Reserve_Price_Data_Selection_Model.dta", clear


****** Reserve Price

probit merge1  logtotal_asset logtotal_employee logtotal_debt pc_percent

predict xb, xb

gen lambda = normalden(xb)/normal(xb)



*** model 1

eststo: xi: reghdfe log_startprice gpc lpc le npc cppcc pc_percent log_meanstartprice logtotal_asset logtotal_employee logtotal_debt log_area usage_year land_purpose   lambda, ab(auction_method year ownership land_level firmid cityid ind) vce(cluster firmcluster) keepsin

*** model 2

eststo: xi: reghdfe log_startprice gpc lpc le npc cppcc gov_friend relative_pc pc_percent log_meanstartprice logtotal_asset logtotal_employee logtotal_debt log_area usage_year land_purpose   lambda , ab( auction_method cityid year ownership land_level firmid ind) vce(cluster firmcluster) keepsin

*** model 3

eststo: xi: reghdfe log_startprice gpc lpc le npc cppcc head log_meanstartprice logtotal_asset logtotal_employee logtotal_debt log_area usage_year land_purpose   lambda, ab( auction_method cityid year ownership land_level firmid ind) vce(cluster firmcluster) keepsin



****** Premium Rate

use "$path/Premium_Data_Selection_Model.dta", clear

probit merge1  logtotal_asset logtotal_employee logtotal_debt pc_percent

predict xb, xb

gen lambda = normalden(xb)/normal(xb)



*** model 4

eststo: xi: reghdfe premiumrate gpc lpc le npc cppcc pc_percent log_meanstartprice  land_purpose   logtotal_asset logtotal_employee logtotal_debt log_area usage_year lambda, ab( year cityid land_level ownership) vce(cluster firmcluster)

*** model 5

eststo: xi: reghdfe premiumrate gpc lpc le npc cppcc gov_friend relative_pc pc_percent log_meanstartprice  land_purpose   logtotal_asset logtotal_employee logtotal_debt log_area lambda, ab( year cityid land_level ownership) vce(cluster firmcluster)

*** model 6

eststo: xi: reghdfe premiumrate gpc lpc le npc cppcc head  pc_percent log_meanstartprice land_purpose   logtotal_asset logtotal_employee logtotal_debt log_area lambda, ab( year cityid land_level  ownership) vce(cluster firmcluster)



******** Table 3

esttab using "$path/Appendix_Table_A6.tex", booktabs se label replace star(* 0.10 ** 0.05 *** 0.01) b(%9.3f) fonttbl(\f0\fnil Arial; )          ///
keep(gpc lpc le npc cppcc) title(Effects of Political Connections on Land Reserve Price and Premium) ///
mgroups(A B, pattern(1 0 0 1 0 0 )                   ///
prefix(\multicolumn{@span}{c}{) suffix(})   ///
span erepeat(\cmidrule(lr){@span}))         ///
alignment(D{.}{.}{-1}) page(dcolumn) nonumber modelwidth(3) varwidth(5)



eststo clear




**********************************************************
**********************************************************
********************* Table A7 ***************************
**********************************************************
**********************************************************

use "$path/Stock_Data_Robust.dta", clear

label variable gov_friend "Friends in Government"
label variable relative_pc "Relative Connections"

******* add more connection variables 

eststo: xi: reghdfe cumulative_abnormal_return0 gpc lpc le npc cppcc  gov_friend relative_pc logtotal_asset logtotal_employee logtotal_debt , ab(provid year ownership) vce(r) keepsin


eststo: xi: reghdfe cumulative_abnormal_return1 gpc lpc le npc cppcc  gov_friend relative_pc logtotal_asset logtotal_employee logtotal_debt , ab(provid year ownership) vce(r) keepsin

eststo: xi: reghdfe cumulative_abnormal_return2 gpc lpc le npc cppcc  gov_friend relative_pc logtotal_asset logtotal_employee logtotal_debt , ab(provid year ownership) vce(r) keepsin


eststo: xi: reghdfe cumulative_abnormal_return3 gpc lpc le npc cppcc  gov_friend relative_pc logtotal_asset logtotal_employee logtotal_debt , ab(provid year ownership) vce(r) keepsin


******** car table 

esttab using "$path/Appendix_Table_A7.tex", se label replace compress star(* 0.10 ** 0.05 *** 0.01) b(%9.3f) /// 
keep(gpc lpc le npc cppcc gov_friend relative_pc) title(Effect of Anti-corruotion on Firms Stock Market Return(Add Government Friends and Relative Connections))


est clear


**********************************************************
**********************************************************
********************* Table A8 ***************************
**********************************************************
**********************************************************


eststo: xi: reghdfe cumulative_abnormal_return0 dis logtotal_asset logtotal_employee logtotal_debt , ab(provid year ownership) vce(r) keepsin

eststo: xi: reghdfe cumulative_abnormal_return1 dis logtotal_asset logtotal_employee logtotal_debt , ab(provid year ownership) vce(r) keepsin

eststo: xi: reghdfe cumulative_abnormal_return2 dis logtotal_asset logtotal_employee logtotal_debt , ab(provid year ownership) vce(r) keepsin

eststo: xi: reghdfe cumulative_abnormal_return3 dis logtotal_asset logtotal_employee logtotal_debt , ab(provid year ownership) vce(r) keepsin



******* 



eststo: xi: reghdfe cumulative_abnormal_return0 pc_dummy logtotal_asset logtotal_employee logtotal_debt , ab(provid year ownership) vce(r) keepsin

eststo: xi: reghdfe cumulative_abnormal_return1 pc_dummy logtotal_asset logtotal_employee logtotal_debt , ab(provid year ownership) vce(r) keepsin

eststo: xi: reghdfe cumulative_abnormal_return2 pc_dummy logtotal_asset logtotal_employee logtotal_debt , ab(provid year ownership) vce(r) keepsin

eststo: xi: reghdfe cumulative_abnormal_return3 pc_dummy logtotal_asset logtotal_employee logtotal_debt , ab(provid year ownership) vce(r) keepsin


***** table 

esttab using "$path/Appendix_Table_A8.tex", se label replace compress star(* 0.10 ** 0.05 *** 0.01) b(%9.3f) /// 
keep(dis pc_dummy) title(Effect of Anti-corruotion on Firms Stock Market Return)

est clear


**********************************************************
**********************************************************
********************* Table A9 ***************************
**********************************************************
**********************************************************




use "$path/Appendix_A5_A9_Data.dta", clear 



***model 1

eststo: xi: reghdfe gov_friend logtotal_asset logtotal_employee logtotal_debt ownership pc_percent, ab(cityid year firmid  ind) vce(cluster cityid firmcluster) keepsin

*** model 2
eststo: xi: reghdfe relative_pc logtotal_asset logtotal_employee logtotal_debt ownership pc_percent, ab( year firmid ind) vce(cluster firmcluster cityid) keepsin


******** Table A9 

esttab using "$path/Appendix_Table_A9.tex", label replace compress star(* 0.10 ** 0.05 *** 0.01) /// 
 title(Determinants of Disclosure of Friends and Relative Connections)

est clear






***********************************************************************
***********************************************************************
*FIGURE B3 IS PRODUCED BY R FILE Appendix_Figure_B3.R
***********************************************************************
***********************************************************************




***********************************************************************
***********************************************************************
*FIGURE B4 
***********************************************************************
***********************************************************************

use "$path/Robust_Data.dta", replace

***** taxation 



eststo: xi: reghdfe log_price gpc lpc tax_contri le pc_percent log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt, ab(auction_method land_purpose  firmid   cityid   year land_level   usage_year ind  ) vce(cluster firmid   cityid  ) keepsin

matrix A[1,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[1,2]= 2*ttail(e(df_r),abs(t))



eststo: xi: reghdfe log_price gpc lpc tax_contri le npc cppcc gov_friend relative_pc pc_percent log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt, ab(auction_method land_purpose   firmid   cityid   year land_level   usage_year ind  ) vce(cluster firmid   cityid  ) keepsin

matrix A[2,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[2,2]= 2*ttail(e(df_r),abs(t))



eststo: xi: reghdfe log_price gpc lpc tax_contri le head pc_percent log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt, ab(auction_method land_purpose   firmid   cityid   year land_level   usage_year ind  ) vce(cluster firmid   cityid  ) keepsin

matrix A[3,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[3,2]= 2*ttail(e(df_r),abs(t))



**** employement 


eststo: xi: reghdfe log_price gpc lpc emp_percent le pc_percent log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt, ab(auction_method land_purpose   firmid   cityid   year land_level   usage_year ind  ) vce(cluster firmid   cityid  ) keepsin

matrix A[4,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[4,2]= 2*ttail(e(df_r),abs(t))




eststo: xi: reghdfe log_price gpc lpc emp_percent le npc cppcc gov_friend relative_pc pc_percent log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt, ab(auction_method land_purpose   firmid   cityid   year land_level   usage_year ind  ) vce(cluster firmid   cityid  ) keepsin

matrix A[5,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[5,2]= 2*ttail(e(df_r),abs(t))



eststo: xi: reghdfe log_price gpc lpc emp_percent le head pc_percent log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt, ab(auction_method land_purpose   firmid   cityid   year land_level   usage_year ind  ) vce(cluster firmid   cityid  ) keepsin

matrix A[6,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[6,2]= 2*ttail(e(df_r),abs(t))




*** productivity

eststo: xi: reghdfe log_price gpc lpc productivity le pc_percent log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt, ab(auction_method land_purpose   firmid   cityid   year ownership land_level   usage_year ind  ) vce(cluster firmid   cityid  ) keepsin

matrix A[7,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[7,2]= 2*ttail(e(df_r),abs(t))




eststo: xi: reghdfe log_price gpc lpc productivity le npc cppcc gov_friend relative_pc pc_percent log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt, ab(auction_method land_purpose   firmid   cityid   year ownership land_level   usage_year ind  ) vce(cluster firmid   cityid  ) keepsin

matrix A[8,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[8,2]= 2*ttail(e(df_r),abs(t))



eststo: xi: reghdfe log_price gpc lpc productivity head le pc_percent log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt, ab(auction_method land_purpose   firmid   cityid   year ownership land_level   usage_year ind  ) vce(cluster firmid   cityid  ) keepsin

matrix A[9,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[9,2]= 2*ttail(e(df_r),abs(t))



***** only real estate
eststo: xi: reghdfe log_price gpc lpc le pc_percent log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt if real_estate==1, ab(auction_method land_purpose   year firmid   cityid   ownership land_level   usage_year) vce(r) keepsin

matrix A[10,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[10,2]= 2*ttail(e(df_r),abs(t))



eststo: xi: reghdfe log_price gpc lpc le pc_percent gov_friend relative_pc log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt if real_estate==1, ab(auction_method land_purpose   year ownership land_level   firmid   cityid   usage_year ind  ) vce(r) keepsin

matrix A[11,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[11,2]= 2*ttail(e(df_r),abs(t))



eststo: xi: reghdfe log_price gpc lpc le pc_percent head log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt if real_estate==1, ab(auction_method land_purpose   year ownership land_level   firmid   cityid   usage_year ind  ) vce(r) keepsin

matrix A[12,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[12,2]= 2*ttail(e(df_r),abs(t))


***** except real estate
eststo: xi: reghdfe log_price gpc lpc le pc_percent log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt if real_estate!=1, ab(auction_method land_purpose   year firmid   cityid   ownership land_level   usage_year) vce(r) keepsin

matrix A[13,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[13,2]= 2*ttail(e(df_r),abs(t))



eststo: xi: reghdfe log_price gpc lpc le pc_percent npc cppcc gov_friend relative_pc log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt if real_estate!=1, ab(auction_method land_purpose   year ownership land_level   firmid   cityid   usage_year ind  ) vce(r) keepsin

matrix A[14,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[14,2]= 2*ttail(e(df_r),abs(t))



eststo: xi: reghdfe log_price gpc lpc le pc_percent head log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt if real_estate!=1, ab(auction_method land_purpose year ownership land_level   firmid   cityid   usage_year ind  ) vce(r) keepsin

matrix A[15,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[15,2]= 2*ttail(e(df_r),abs(t))



**** Get rid of Beijing Shanghai Tianjing Guangzhou


eststo: xi: reghdfe log_price gpc lpc le pc_percent log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt if normal==1, ab(auction_method land_purpose   year firmid   ownership land_level   usage_year ind  ) vce(r) keepsin

matrix A[16,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[16,2]= 2*ttail(e(df_r),abs(t))


eststo: xi: reghdfe log_price gpc lpc le pc_percent npc cppcc gov_friend relative_pc log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt if normal==1, ab(auction_method land_purpose   year firmid   ownership land_level   usage_year ind  ) vce(r) keepsin

matrix A[17,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[17,2]= 2*ttail(e(df_r),abs(t))



eststo: xi: reghdfe log_price gpc lpc le head pc_percent log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt if normal==1, ab(auction_method land_purpose   year firmid   ownership land_level   usage_year ind  ) vce(r) keepsin

matrix A[18,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[18,2]= 2*ttail(e(df_r),abs(t))


********* gdp per capita as institution quality



eststo: xi: reghdfe log_price gpc lpc le log_gdp pc_percent log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt, ab(auction_method land_purpose   cityid   year firmid   ownership land_level   usage_year ind  ) vce(r) keepsin

matrix A[19,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[19,2]= 2*ttail(e(df_r),abs(t))



eststo: xi: reghdfe log_price gpc lpc le log_gdp npc cppcc gov_friend relative_pc pc_percent log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt, ab(auction_method land_purpose   cityid   year firmid   ownership land_level   usage_year ind  ) vce(r) keepsin

matrix A[20,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[20,2]= 2*ttail(e(df_r),abs(t))



eststo: xi: reghdfe log_price gpc lpc le log_gdp head pc_percent log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt, ab(auction_method land_purpose   cityid   year firmid   ownership land_level   usage_year ind  ) vce(r) keepsin

matrix A[21,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[21,2]= 2*ttail(e(df_r),abs(t))



********* adding institutional quality


eststo: xi: reghdfe log_price gpc lpc le ins_quality pc_percent log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt, ab(auction_method land_purpose   cityid   year firmid   ownership land_level   usage_year ind  ) vce(r) keepsin

matrix A[22,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[22,2]= 2*ttail(e(df_r),abs(t))



eststo: xi: reghdfe log_price gpc lpc le ins_quality npc cppcc gov_friend relative_pc pc_percent log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt, ab(auction_method land_purpose    cityid   year firmid   ownership land_level   usage_year ind  ) vce(r) keepsin

matrix A[23,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[23,2]= 2*ttail(e(df_r),abs(t))



eststo: xi: reghdfe log_price gpc lpc le ins_quality head pc_percent log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt, ab(auction_method land_purpose    cityid   year firmid   ownership land_level   usage_year ind  ) vce(r) keepsin

matrix A[24,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[24,2]= 2*ttail(e(df_r),abs(t))





********* adding patent as proxy for institutional


eststo: xi: reghdfe log_price gpc lpc le patent_per pc_percent log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt, ab(auction_method land_purpose    cityid   year firmid   ownership land_level   usage_year ind  ) vce(r) keepsin

matrix A[25,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[25,2]= 2*ttail(e(df_r),abs(t))



eststo: xi: reghdfe log_price gpc lpc le patent_per npc cppcc gov_friend relative_pc pc_percent log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt, ab(auction_method land_purpose    cityid   year firmid   ownership land_level   usage_year ind  ) vce(r) keepsin

matrix A[26,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[26,2]= 2*ttail(e(df_r),abs(t))



eststo: xi: reghdfe log_price gpc lpc le patent_per head pc_percent log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt, ab(auction_method land_purpose    cityid   year firmid   ownership land_level   usage_year ind  ) vce(r) keepsin

matrix A[27,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[27,2]= 2*ttail(e(df_r),abs(t))




********** adding corruption cases per capita


eststo: xi: reghdfe log_price gpc lpc le cop_case pc_percent log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt, ab(auction_method land_purpose    cityid   year firmid   ownership land_level   usage_year ind  ) vce(r) keepsin

matrix A[28,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[28,2]= 2*ttail(e(df_r),abs(t))




eststo: xi: reghdfe log_price gpc lpc le cop_case npc cppcc gov_friend relative_pc pc_percent log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt, ab(auction_method land_purpose    cityid   year firmid   ownership land_level   usage_year ind  ) vce(r) keepsin

matrix A[29,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[29,2]= 2*ttail(e(df_r),abs(t))




eststo: xi: reghdfe log_price gpc lpc le cop_case head pc_percent log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt, ab(auction_method land_purpose    cityid   year firmid   ownership land_level   usage_year ind  ) vce(r) keepsin

matrix A[30,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[30,2]= 2*ttail(e(df_r),abs(t))

*** total area 


eststo: xi: reghdfe log_price gpc lpc le logtotal_area pc_percent log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt, ab(auction_method land_purpose    cityid   year firmid   ownership land_level   usage_year ind  ) vce(r) keepsin


matrix A[31,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[31,2]= 2*ttail(e(df_r),abs(t))




eststo: xi: reghdfe log_price gpc lpc le logtotal_area npc cppcc gov_friend relative_pc pc_percent log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt, ab(auction_method land_purpose  cityid   year firmid   ownership land_level   usage_year ind  ) vce(r) keepsin

matrix A[32,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[32,2]= 2*ttail(e(df_r),abs(t))




eststo: xi: reghdfe log_price gpc lpc le logtotal_area head pc_percent log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt, ab(auction_method land_purpose  cityid   year firmid   ownership land_level   usage_year ind  ) vce(r) keepsin

matrix A[33,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[33,2]= 2*ttail(e(df_r),abs(t))


********* adding city-year fixed effect 


eststo: xi: reghdfe log_price gpc lpc le pc_percent log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt, ab(auction_method land_purpose  city_year firmid   ownership land_level   usage_year ind  ) vce(r) keepsin

matrix A[34,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[34,2]= 2*ttail(e(df_r),abs(t))


eststo: xi: reghdfe log_price gpc lpc le pc_percent npc cppcc gov_friend relative_pc log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt, ab(auction_method land_purpose  city_year firmid   ownership land_level   usage_year ind  ) vce(r) keepsin

matrix A[35,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[35,2]= 2*ttail(e(df_r),abs(t))


eststo: xi: reghdfe log_price gpc lpc le head pc_percent log_meanprice log_area logtotal_asset logtotal_employee logtotal_debt, ab(auction_method land_purpose  city_year firmid   ownership land_level   usage_year ind  ) vce(r) keepsin

matrix A[36,1]=_b[lpc]
scalar t=_b[lpc]/_se[lpc]
matrix A[36,2]= 2*ttail(e(df_r),abs(t))

*** figure 

svmat A, names(col)

seq b, f(1) t(12) b(3)


graph twoway (scatter c1 c2 if b==1, jitter(15) msymbol(O)) (scatter c1 c2 if b==2, jitter(15) msymbol(D)) (scatter c1 c2 if b==3, jitter(15) msymbol(T)) (scatter c1 c2 if b==4, jitter(15) msymbol(S)) (scatter c1 c2 if b==5, jitter(15) msymbol(o)) (scatter c1 c2 if b==6, jitter(15) msymbol(t)) (scatter c1 c2 if b==7, jitter(15) msymbol(s)) (scatter c1 c2 if b==8, jitter(30) msymbol(Oh)) (scatter c1 c2 if b==9, jitter(30) msymbol(Dh)) (scatter c1 c2 if b==10, jitter(30) msymbol(Th)) (scatter c1 c2 if b==11, jitter(30) msymbol(Sh)) (scatter c1 c2 if b==12, jitter(30) msymbol(+)),  ///
  legend(label(1 Taxation) label(2 Employment) label(3 Productivity) label(4 Real Estate) label(5 Except Real Estate) label(6 No Major Cities) label(7 Institution1) label(8 Institution2) label(9 Institution3) label(10 Institution4) label(11 Land Supply) label(12 City-Year) cols(4)) xtitle("P-value from Each Model") ytitle("LPC Coefficient") graphregion(color(white))
  

